US8625904B2 - Detecting recurring themes in consumer image collections - Google Patents
Detecting recurring themes in consumer image collections Download PDFInfo
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- US8625904B2 US8625904B2 US13/221,078 US201113221078A US8625904B2 US 8625904 B2 US8625904 B2 US 8625904B2 US 201113221078 A US201113221078 A US 201113221078A US 8625904 B2 US8625904 B2 US 8625904B2
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- digital images
- frequent
- frequent itemsets
- feature descriptors
- image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
- G06F16/438—Presentation of query results
- G06F16/4387—Presentation of query results by the use of playlists
- G06F16/4393—Multimedia presentations, e.g. slide shows, multimedia albums
Abstract
Description
TABLE 1 |
Example feature descriptors |
Feature Category | Example Feature Descriptors |
Scene Class Features | NatureScene, CityBuildings, Beach, Indoor, |
Sunset | |
Materials Class Features | Sky, Snow, Water, FallFoliage, |
GreenFoliage, Text | |
Low-Level Features | Blue, Green, Red, Yellow, Pink, Purple, |
White | |
Facial Features | NoFace, OnePerson, GroupOf2, |
3OrMoreFaces, SmallFace, LargeFace, | |
Baby, Adult, Female, Male | |
Time-Based Features | TimeOfDay, TimeOfYear, TimeOfWeek |
Capture Condition Features | Closeup, Backlight, Dark |
Metadata Tag Features | People Names, Geographic Location |
cover(F)={τετ|F⊂τ} (1)
That is, the cover of F consists of the set of transactions τ in the transactional database τ containing the items in the itemset F, and therefore are to be counted towards the frequency of F. The “support of F” is defined to be the size of the cover of F (i.e., the number of transactions in the cover):
support(F)=|cover(F)| (2)
where the operator |A| returns the number of elements in the set A.
Φ={F|support(F)≧minsup} (3)
That is, the set of itemsets having a support that is greater than or equal the specified minimum support. The minimum support is specified to be in the interval [0,1] as a fraction of the total number of transactions in the database. A frequent itemset is maximal if there are no proper supersets of F in Γ that are also frequent.
confidence(F)=support(F)/Σj=1 mNumImages(Eventj) (4)
where Event1, Event2, . . . , Eventm are the events in which the digital images in the cover of F are included, and the function NumImages(Eventj) provides the number of images in the specified event. Therefore, the
quality(F)=1/Πi=1 n p(f i) (5)
where the frequent itemset 510 (F) contains feature descriptors f1, f2, . . . fn, and p(fi) is the probability of occurrence of fi. Those skilled in the art will recognize that the product of the probability of occurrences of the relevant feature descriptors represents an estimate of a probability of occurrence for the frequent itemset under the assumption that the feature descriptors are independent. In other embodiments, joint probability distributions can be used to account for any correlations that may exist between the feature descriptors.
- 10 computer
- 20 processor unit
- 30 memory unit
- 50 input device
- 60 keyboard
- 70 display device
- 80 network connection
- 110 digital image collection
- 115 digital image
- 120 generate feature descriptors step
- 130 feature descriptors
- 140 store in metadata database step
- 145 metadata database
- 150 frequent pattern mining step
- 155 frequent itemsets
- 160 filter frequent itemsets step
- 170 rank frequent itemsets step
- 175 ranked frequent itemsets
- 180 select related image groups
- 185 related image groups
- 300 analyze image content step
- 302 scene class features
- 304 materials class features
- 306 low-level features
- 308 facial features
- 310 analyze capture metadata step
- 312 time-based features
- 314 capture condition features
- 316 metadata tag features
- 320 quantize features step
- 410 event clustering step
- 415 event clusters
- 420 extract events in itemset cover step
- 430 compute confidence score step
- 440 confidence score
- 450 confidence score test
- 460 remove frequent itemset step
- 500 compute quality scores step
- 510 quality scores
- 520 probability distributions
- 530 compute final ranked list step
- 540 application-specific requirements
- 600 transactional database
- 610 transaction ID
- 620 feature descriptors
Claims (21)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/221,078 US8625904B2 (en) | 2011-08-30 | 2011-08-30 | Detecting recurring themes in consumer image collections |
PCT/US2012/051541 WO2013032755A1 (en) | 2011-08-30 | 2012-08-20 | Detecting recurring themes in consumer image collections |
US14/146,886 US9042646B2 (en) | 2011-08-30 | 2014-01-03 | Detecting recurring themes in consumer image collections |
Applications Claiming Priority (1)
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US13/221,078 US8625904B2 (en) | 2011-08-30 | 2011-08-30 | Detecting recurring themes in consumer image collections |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
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US14/146,886 Continuation US9042646B2 (en) | 2011-08-30 | 2014-01-03 | Detecting recurring themes in consumer image collections |
Publications (2)
Publication Number | Publication Date |
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US20130051670A1 US20130051670A1 (en) | 2013-02-28 |
US8625904B2 true US8625904B2 (en) | 2014-01-07 |
Family
ID=46964012
Family Applications (2)
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US13/221,078 Expired - Fee Related US8625904B2 (en) | 2011-08-30 | 2011-08-30 | Detecting recurring themes in consumer image collections |
US14/146,886 Expired - Fee Related US9042646B2 (en) | 2011-08-30 | 2014-01-03 | Detecting recurring themes in consumer image collections |
Family Applications After (1)
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US14/146,886 Expired - Fee Related US9042646B2 (en) | 2011-08-30 | 2014-01-03 | Detecting recurring themes in consumer image collections |
Country Status (2)
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US (2) | US8625904B2 (en) |
WO (1) | WO2013032755A1 (en) |
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US10007679B2 (en) | 2008-08-08 | 2018-06-26 | The Research Foundation For The State University Of New York | Enhanced max margin learning on multimodal data mining in a multimedia database |
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Cited By (13)
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---|---|---|---|---|
US9038117B2 (en) | 2006-03-01 | 2015-05-19 | Martin Kelly Jones | Systems and methods for automated media programming (AMP) |
US20080320522A1 (en) * | 2006-03-01 | 2008-12-25 | Martin Kelly Jones | Systems and Methods for Automated Media Programming (AMP) |
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US20130287292A1 (en) * | 2012-04-27 | 2013-10-31 | Kabushiki Kaisha Toshiba | Image processing apparatus, image processing method and computer program product |
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US9690990B2 (en) * | 2014-12-31 | 2017-06-27 | Fih (Hong Kong) Limited | Electronic device and method for classifing images |
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Also Published As
Publication number | Publication date |
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US20130051670A1 (en) | 2013-02-28 |
WO2013032755A1 (en) | 2013-03-07 |
US20140119652A1 (en) | 2014-05-01 |
US9042646B2 (en) | 2015-05-26 |
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